EYE-DEX: Eye Disease Detection and EXplanation System
This work addresses the problem of time-consuming and subjective manual grading of retinal images for ophthalmologists, though it is incremental as it benchmarks existing CNN models on a new dataset.
The study tackled automated retinal disease diagnosis by presenting EYE-DEX, a framework that classified 10 retinal conditions using a dataset of 21,577 fundus images, achieving a state-of-the-art test accuracy of 92.36% with a finetuned VGG16 model and integrating Grad-CAM for visual explanations to enhance clinician trust.
Retinal disease diagnosis is critical in preventing vision loss and reducing socioeconomic burdens. Globally, over 2.2 billion people are affected by some form of vision impairment, resulting in annual productivity losses estimated at $411 billion. Traditional manual grading of retinal fundus images by ophthalmologists is time-consuming and subjective. In contrast, deep learning has revolutionized medical diagnostics by automating retinal image analysis and achieving expert-level performance. In this study, we present EYE-DEX, an automated framework for classifying 10 retinal conditions using the large-scale Retinal Disease Dataset comprising 21,577 eye fundus images. We benchmark three pre-trained Convolutional Neural Network (CNN) models--VGG16, VGG19, and ResNet50--with our finetuned VGG16 achieving a state-of-the-art global benchmark test accuracy of 92.36%. To enhance transparency and explainability, we integrate the Gradient-weighted Class Activation Mapping (Grad-CAM) technique to generate visual explanations highlighting disease-specific regions, thereby fostering clinician trust and reliability in AI-assisted diagnostics.